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arXiv · 2502.15012

Graph in the Vault: Protecting Edge GNN Inference with Trusted Execution Environment

Ruyi Ding·Tianhong Xu·Aidong Adam Ding·Yunsi Fei
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Abstract

Wide deployment of machine learning models on edge devices has rendered the model intellectual property (IP) and data privacy vulnerable. We propose GNNVault, the first secure Graph Neural Network (GNN) deployment strategy based on Trusted Execution Environment (TEE). GNNVault follows the design of 'partition-before-training' and includes a private GNN rectifier to complement with a public backbone model. This way, both critical GNN model parameters and the private graph used during inference are protected within secure TEE compartments. Real-world implementations with Intel SGX demonstrate that GNNVault safeguards GNN inference against state-of-the-art link stealing attacks with negligible accuracy degradation (<2%).

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Ruyi Ding, Tianhong Xu, Aidong Adam Ding, Yunsi Fei. 2025-02-20. Graph in the Vault: Protecting Edge GNN Inference with Trusted Execution Environment. https://arxiv.org/abs/2502.15012

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